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Record W2103146842 · doi:10.1080/10888438.2015.1077447

Examining the Cross-Lagged Relationships Between RAN and Word Reading in Chinese

2015· article· en· W2103146842 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientific Studies of Reading · 2015
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReading (process)FluencyRanPsychologyWord recognitionWord (group theory)Rapid automatized namingLinguisticsMandarin ChinesePhonological awarenessCognitive psychologyComputer scienceMathematics education

Abstract

fetched live from OpenAlex

The purpose of this 4-year longitudinal study was to specify the direction of the relationship between RAN and word reading (accuracy and fluency) in Chinese. This is important in light of arguments that the developmental relationships between RAN and reading can disclose changes in the reading processes underlying reading as development proceeds. One hundred thirty-five Mandarin-speaking Chinese children (65 boys, 70 girls; M age = 96.40 months) were assessed on RAN (digits), word-reading accuracy (Character Recognition), and word-reading fluency (One-Minute Reading). The children were assessed on the same measures when they were in Grades 3, 4, and 5. The results of path analysis indicated that the effects between RAN and word reading were unidirectional and subject to the type of reading outcome: Only RAN predicted word reading and only when word reading was operationalized with a reading fluency measure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.200
GPT teacher head0.406
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it